{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import tushare as ts\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pro = ts.pro_api()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sw_index=pro.index_basic(market='SW')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sw_indices = sw_index.ts_code.str.split('.', expand=True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind_index = sw_index.query('category==\"一级行业指数\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Josh\\AppData\\Local\\conda\\conda\\envs\\cvxpyenv\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "ind_index['name'] = ind_index['name'].str.split('(', expand=True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Josh\\AppData\\Local\\conda\\conda\\envs\\cvxpyenv\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "ind_index['symbol'] = ind_index.ts_code.str.split('.', expand=True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'农林牧渔': '801010',\n",
       " '采掘': '801020',\n",
       " '化工': '801030',\n",
       " '钢铁': '801040',\n",
       " '有色金属': '801050',\n",
       " '建筑建材': '801060',\n",
       " '机械设备': '801890',\n",
       " '电子': '801080',\n",
       " '交运设备': '801090',\n",
       " '信息设备': '801100',\n",
       " '家用电器': '801110',\n",
       " '食品饮料': '801120',\n",
       " '纺织服装': '801130',\n",
       " '轻工制造': '801140',\n",
       " '医药生物': '801150',\n",
       " '公用事业': '801160',\n",
       " '交通运输': '801170',\n",
       " '房地产': '801180',\n",
       " '金融服务': '801190',\n",
       " '商业贸易': '801200',\n",
       " '休闲服务': '801210',\n",
       " '信息服务': '801220',\n",
       " '综合': '801230',\n",
       " '建筑材料': '801710',\n",
       " '建筑装饰': '801720',\n",
       " '电气设备': '801730',\n",
       " '国防军工': '801740',\n",
       " '计算机': '801750',\n",
       " '传媒': '801760',\n",
       " '通信': '801770',\n",
       " '银行': '801780',\n",
       " '非银金融': '801790',\n",
       " '汽车': '801880'}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_index.set_index('name')['symbol'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>name</th>\n",
       "      <th>market</th>\n",
       "      <th>publisher</th>\n",
       "      <th>category</th>\n",
       "      <th>base_date</th>\n",
       "      <th>base_point</th>\n",
       "      <th>list_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>801010.SI</td>\n",
       "      <td>农林牧渔(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20031015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>801020.SI</td>\n",
       "      <td>采掘(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20031015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>801030.SI</td>\n",
       "      <td>化工(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20031015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>801040.SI</td>\n",
       "      <td>钢铁(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20031015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>801050.SI</td>\n",
       "      <td>有色金属(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20031015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>801060.SI</td>\n",
       "      <td>建筑建材(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>801070.SI</td>\n",
       "      <td>机械设备(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>801080.SI</td>\n",
       "      <td>电子(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20031015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>801090.SI</td>\n",
       "      <td>交运设备(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>801100.SI</td>\n",
       "      <td>信息设备(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>801110.SI</td>\n",
       "      <td>家用电器(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20031015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>801120.SI</td>\n",
       "      <td>食品饮料(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20031015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>801130.SI</td>\n",
       "      <td>纺织服装(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20031015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>801140.SI</td>\n",
       "      <td>轻工制造(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20031015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>801150.SI</td>\n",
       "      <td>医药生物(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20031015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>801160.SI</td>\n",
       "      <td>公用事业(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20031015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>801170.SI</td>\n",
       "      <td>交通运输(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20031015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>801180.SI</td>\n",
       "      <td>房地产(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20031015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>801190.SI</td>\n",
       "      <td>金融服务(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>801200.SI</td>\n",
       "      <td>商业贸易(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20031015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>801210.SI</td>\n",
       "      <td>休闲服务(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20031015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>801220.SI</td>\n",
       "      <td>信息服务(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>801230.SI</td>\n",
       "      <td>综合(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20031015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>801710.SI</td>\n",
       "      <td>建筑材料(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20140221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>801720.SI</td>\n",
       "      <td>建筑装饰(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20140221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>801730.SI</td>\n",
       "      <td>电气设备(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20140221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>801740.SI</td>\n",
       "      <td>国防军工(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20140221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>801750.SI</td>\n",
       "      <td>计算机(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20140221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>801760.SI</td>\n",
       "      <td>传媒(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20140221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>801770.SI</td>\n",
       "      <td>通信(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20140221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>801780.SI</td>\n",
       "      <td>银行(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20140221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>801790.SI</td>\n",
       "      <td>非银金融(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20140221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>801880.SI</td>\n",
       "      <td>汽车(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20140221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>801890.SI</td>\n",
       "      <td>机械设备(申万)</td>\n",
       "      <td>SW</td>\n",
       "      <td>申万研究</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>19991230</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20140221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ts_code      name market publisher category base_date  base_point  \\\n",
       "5    801010.SI  农林牧渔(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "14   801020.SI    采掘(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "19   801030.SI    化工(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "27   801040.SI    钢铁(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "30   801050.SI  有色金属(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "36   801060.SI  建筑建材(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "39   801070.SI  机械设备(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "46   801080.SI    电子(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "52   801090.SI  交运设备(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "57   801100.SI  信息设备(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "60   801110.SI  家用电器(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "63   801120.SI  食品饮料(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "68   801130.SI  纺织服装(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "71   801140.SI  轻工制造(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "76   801150.SI  医药生物(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "83   801160.SI  公用事业(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "88   801170.SI  交通运输(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "97   801180.SI   房地产(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "100  801190.SI  金融服务(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "105  801200.SI  商业贸易(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "111  801210.SI  休闲服务(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "117  801220.SI  信息服务(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "122  801230.SI    综合(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "152  801710.SI  建筑材料(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "156  801720.SI  建筑装饰(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "162  801730.SI  电气设备(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "167  801740.SI  国防军工(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "172  801750.SI   计算机(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "175  801760.SI    传媒(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "177  801770.SI    通信(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "178  801780.SI    银行(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "179  801790.SI  非银金融(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "198  801880.SI    汽车(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "200  801890.SI  机械设备(申万)     SW      申万研究   一级行业指数  19991230      1000.0   \n",
       "\n",
       "    list_date  \n",
       "5    20031015  \n",
       "14   20031015  \n",
       "19   20031015  \n",
       "27   20031015  \n",
       "30   20031015  \n",
       "36       None  \n",
       "39       None  \n",
       "46   20031015  \n",
       "52       None  \n",
       "57       None  \n",
       "60   20031015  \n",
       "63   20031015  \n",
       "68   20031015  \n",
       "71   20031015  \n",
       "76   20031015  \n",
       "83   20031015  \n",
       "88   20031015  \n",
       "97   20031015  \n",
       "100      None  \n",
       "105  20031015  \n",
       "111  20031015  \n",
       "117      None  \n",
       "122  20031015  \n",
       "152  20140221  \n",
       "156  20140221  \n",
       "162  20140221  \n",
       "167  20140221  \n",
       "172  20140221  \n",
       "175  20140221  \n",
       "177  20140221  \n",
       "178  20140221  \n",
       "179  20140221  \n",
       "198  20140221  \n",
       "200  20140221  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sw_index.query('category==\"一级行业指数\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = sw_indices.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sw_index_hist(indices, start, end):\n",
    "    \"\"\"\n",
    "    Get historical values for Shen-Wan indices\n",
    "    \"\"\"\n",
    "    url_pat = 'http://www.swsindex.com/excel2.aspx?ctable=swindexhistory&where=%20%20swindexcode%20in%20{indices}%20and%20%20BargainDate%3E=%27{start}%27%20and%20%20BargainDate%3C=%27{end}%27'\n",
    "\n",
    "    def _wrap_in_bracket(indices):\n",
    "        return '(' + ','.join('%27'+i+'%27' for i in indices) + ')'\n",
    "    \n",
    "    if isinstance(indices, str):\n",
    "        indices = [indices]\n",
    "    \n",
    "    url = url_pat.format(indices=_wrap_in_bracket(indices), start=start, end=end)\n",
    "    response = requests.get(url)\n",
    "    indexDf = pd.read_html(response.content.decode('utf-8'))[0]\n",
    "\n",
    "    RENAME = {'指数代码':'symbol', \n",
    "              '指数名称':'name',\n",
    "              '发布日期':'date', \n",
    "              '开盘指数':'open',\n",
    "              '最高指数':'high',\n",
    "              '最低指数':'low',\n",
    "              '收盘指数':'close',\n",
    "              '成交量(亿股)':'volume',\n",
    "              '成交额(亿元)':'amount', \n",
    "              '涨跌幅(%)':'pct_chg'}\n",
    "\n",
    "    indexDf = indexDf.rename(columns = RENAME)\n",
    "\n",
    "    indexDf['date'] = pd.to_datetime(indexDf.date)\n",
    "\n",
    "    MULTIPLIER = 1e8\n",
    "\n",
    "    indexDf[['volume', 'amount']] *= MULTIPLIER\n",
    "    \n",
    "    return indexDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\josh\\source\\repos\\arctic\\arctic\\store\\_pandas_ndarray_store.py:6: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
      "  from pandas import DataFrame, Series, Panel\n"
     ]
    }
   ],
   "source": [
    "from arctic import CHUNK_STORE, Arctic\n",
    "a = Arctic('localhost')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Library created, but couldn't enable sharding: no such command: 'enablesharding'. This is OK if you're not 'admin'\n"
     ]
    }
   ],
   "source": [
    "a.initialize_library('index', CHUNK_STORE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "lib_index = a['index']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 507/507 [16:50<00:00,  1.99s/it]\n"
     ]
    }
   ],
   "source": [
    "for index in tqdm(sw_indices):\n",
    "    df = get_sw_index_hist(index, '2014-01-01', '2020-10-23')\n",
    "    df = df.set_index('date')\n",
    "    lib_index.write(index, df, chunk_size='M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sw_industry_class():\n",
    "    \"\"\"\n",
    "    Returns Shen-Wan indsutry classification table\n",
    "    \"\"\"\n",
    "    industry = pd.read_html('./refData/SwClass.xls')[0]\n",
    "    IND_RENAME = {\n",
    "        '行业名称':'industry', \n",
    "        '股票代码':'symbol', \n",
    "        '股票名称':'name', \n",
    "        '起始日期':'start_date', \n",
    "        '结束日期':'end_date',\n",
    "    }\n",
    "\n",
    "    industry = industry.rename(columns=IND_RENAME)\n",
    "    industry['symbol'] = industry.symbol.astype(str).str.zfill(6)\n",
    "    \n",
    "    return industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'http://www.csindex.com.cn/zh-CN/downloads/industry-class'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = requests.get(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "soup = BeautifulSoup(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<a class=\"stats_a\" href=\"http://www.csindex.com.cn/uploads/downloads/other/files/zh_CN/other_download7.pdf?t=1603973350\" target=\"_blank\">关于中证行业分类的说明<i class=\"i_icon_pdf\"></i></a>,\n",
       " <a class=\"stats_a\" href=\"http://www.csindex.com.cn/uploads/downloads/other/files/zh_CN/other_download10.xlsx?t=1603973350\" target=\"_blank\">中证行业分类架构<i class=\"i_icon_excel\"></i></a>,\n",
       " <a class=\"stats_a\" href=\"http://www.csindex.com.cn/uploads/downloads/other/files/zh_CN/other_download34.xls?t=1603973350\" target=\"_blank\">证监会行业分类标准<i class=\"i_icon_excel\"></i></a>,\n",
       " <a class=\"stats_a\" href=\"http://www.csindex.com.cn/uploads/downloads/other/files/zh_CN/csrcindustry.zip?t=1603973350\" target=\"_blank\">证监会行业分类表<i class=\"i_icon_rar\"></i></a>,\n",
       " <a class=\"stats_a\" href=\"http://www.csindex.com.cn/uploads/downloads/other/files/zh_CN/ZzhyflWz.zip?t=1603973350\" target=\"_blank\">中证行业分类表<i class=\"i_icon_rar\"></i></a>,\n",
       " <a class=\"stats_a\" href=\"http://www.csindex.com.cn/uploads/downloads/other/files/zh_CN/CSRChyflWzDay.zip?t=1603973350\" target=\"_blank\">证监会行业分类每日变更表<i class=\"i_icon_rar\"></i></a>,\n",
       " <a class=\"stats_a\" href=\"http://www.csindex.com.cn/uploads/downloads/other/files/zh_CN/ZzhyflWzDay.zip?t=1603973350\" target=\"_blank\">中证行业分类每日变更表<i class=\"i_icon_rar\"></i></a>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soup.find_all('a', {'class' : 'stats_a'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cics_industry():\n",
    "    from io import StringIO, BytesIO\n",
    "    from zipfile import ZipFile\n",
    "    import pandas as pd\n",
    "\n",
    "    url = 'http://www.csindex.com.cn/uploads/downloads/other/files/zh_CN/ZzhyflWz.zip'\n",
    "    response = requests.get(url)\n",
    "    t = ZipFile(BytesIO(response.content))\n",
    "    cics_industry = pd.read_excel( t.open('cicslevel2.xls') )\n",
    "    cics_industry.columns = [c.split('\\n')[1] for c in cics_industry]\n",
    "    cics_industry['Securities Code'] = cics_industry['Securities Code'].astype(str).str.zfill(6)\n",
    "    \n",
    "    return cics_industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Industrials                   1083\n",
       "Consumer Discretionary         671\n",
       "Information Technology         662\n",
       "Materials                      596\n",
       "Health Care                    369\n",
       "Financials                     261\n",
       "Consumer Staples               223\n",
       "Utilities                      117\n",
       "Telecommunication Services      97\n",
       "Energy                          80\n",
       "Name: CICS 1st Level Name(Eng.), dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cics_industry['CICS 1st Level Name(Eng.)'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pro = ts.pro_api()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "symbols = [ \"{:06d}\".format(i) for i in range(908, 918) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n"
     ]
    }
   ],
   "source": [
    "df = pd.concat([ts.get_k_data(sym, index=True) for sym in symbols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['symbol'] = df['code'].str[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "close_prices = df.pivot_table('close', 'date', 'symbol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "returns = close_prices.pct_change().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
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       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row0_col6\" class=\"data row0 col6\" >0.774201</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row0_col7\" class=\"data row0 col7\" >0.47225</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row0_col8\" class=\"data row0 col8\" >0.373288</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row0_col9\" class=\"data row0 col9\" >0.605519</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8level0_row1\" class=\"row_heading level0 row1\" >000909</th>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row1_col0\" class=\"data row1 col0\" >0.754443</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row1_col1\" class=\"data row1 col1\" >1</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row1_col2\" class=\"data row1 col2\" >0.879473</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row1_col3\" class=\"data row1 col3\" >0.774717</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row1_col4\" class=\"data row1 col4\" >0.64737</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row1_col5\" class=\"data row1 col5\" >0.585195</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row1_col6\" class=\"data row1 col6\" >0.771695</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row1_col7\" class=\"data row1 col7\" >0.574787</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row1_col8\" class=\"data row1 col8\" >0.419327</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row1_col9\" class=\"data row1 col9\" >0.607093</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8level0_row2\" class=\"row_heading level0 row2\" >000910</th>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row2_col0\" class=\"data row2 col0\" >0.763462</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row2_col1\" class=\"data row2 col1\" >0.879473</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row2_col2\" class=\"data row2 col2\" >1</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row2_col3\" class=\"data row2 col3\" >0.839303</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row2_col4\" class=\"data row2 col4\" >0.681701</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row2_col5\" class=\"data row2 col5\" >0.628888</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row2_col6\" class=\"data row2 col6\" >0.819263</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row2_col7\" class=\"data row2 col7\" >0.631557</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row2_col8\" class=\"data row2 col8\" >0.452571</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row2_col9\" class=\"data row2 col9\" >0.659413</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8level0_row3\" class=\"row_heading level0 row3\" >000911</th>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row3_col0\" class=\"data row3 col0\" >0.658666</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row3_col1\" class=\"data row3 col1\" >0.774717</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row3_col2\" class=\"data row3 col2\" >0.839303</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row3_col3\" class=\"data row3 col3\" >1</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row3_col4\" class=\"data row3 col4\" >0.779595</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row3_col5\" class=\"data row3 col5\" >0.687012</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row3_col6\" class=\"data row3 col6\" >0.775302</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row3_col7\" class=\"data row3 col7\" >0.571662</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row3_col8\" class=\"data row3 col8\" >0.393749</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row3_col9\" class=\"data row3 col9\" >0.573692</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8level0_row4\" class=\"row_heading level0 row4\" >000912</th>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row4_col0\" class=\"data row4 col0\" >0.523497</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row4_col1\" class=\"data row4 col1\" >0.64737</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row4_col2\" class=\"data row4 col2\" >0.681701</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row4_col3\" class=\"data row4 col3\" >0.779595</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row4_col4\" class=\"data row4 col4\" >1</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row4_col5\" class=\"data row4 col5\" >0.726244</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row4_col6\" class=\"data row4 col6\" >0.629757</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row4_col7\" class=\"data row4 col7\" >0.454021</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row4_col8\" class=\"data row4 col8\" >0.298944</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row4_col9\" class=\"data row4 col9\" >0.496394</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8level0_row5\" class=\"row_heading level0 row5\" >000913</th>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row5_col0\" class=\"data row5 col0\" >0.414231</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row5_col1\" class=\"data row5 col1\" >0.585195</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row5_col2\" class=\"data row5 col2\" >0.628888</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row5_col3\" class=\"data row5 col3\" >0.687012</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row5_col4\" class=\"data row5 col4\" >0.726244</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row5_col5\" class=\"data row5 col5\" >1</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row5_col6\" class=\"data row5 col6\" >0.489292</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row5_col7\" class=\"data row5 col7\" >0.459289</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row5_col8\" class=\"data row5 col8\" >0.277598</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row5_col9\" class=\"data row5 col9\" >0.420203</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8level0_row6\" class=\"row_heading level0 row6\" >000914</th>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row6_col0\" class=\"data row6 col0\" >0.774201</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row6_col1\" class=\"data row6 col1\" >0.771695</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row6_col2\" class=\"data row6 col2\" >0.819263</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row6_col3\" class=\"data row6 col3\" >0.775302</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row6_col4\" class=\"data row6 col4\" >0.629757</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row6_col5\" class=\"data row6 col5\" >0.489292</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row6_col6\" class=\"data row6 col6\" >1</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row6_col7\" class=\"data row6 col7\" >0.549196</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row6_col8\" class=\"data row6 col8\" >0.420442</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row6_col9\" class=\"data row6 col9\" >0.618028</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8level0_row7\" class=\"row_heading level0 row7\" >000915</th>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row7_col0\" class=\"data row7 col0\" >0.47225</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row7_col1\" class=\"data row7 col1\" >0.574787</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row7_col2\" class=\"data row7 col2\" >0.631557</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row7_col3\" class=\"data row7 col3\" >0.571662</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row7_col4\" class=\"data row7 col4\" >0.454021</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row7_col5\" class=\"data row7 col5\" >0.459289</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row7_col6\" class=\"data row7 col6\" >0.549196</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row7_col7\" class=\"data row7 col7\" >1</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row7_col8\" class=\"data row7 col8\" >0.854445</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row7_col9\" class=\"data row7 col9\" >0.424576</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8level0_row8\" class=\"row_heading level0 row8\" >000916</th>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row8_col0\" class=\"data row8 col0\" >0.373288</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row8_col1\" class=\"data row8 col1\" >0.419327</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row8_col2\" class=\"data row8 col2\" >0.452571</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row8_col3\" class=\"data row8 col3\" >0.393749</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row8_col4\" class=\"data row8 col4\" >0.298944</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row8_col5\" class=\"data row8 col5\" >0.277598</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row8_col6\" class=\"data row8 col6\" >0.420442</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row8_col7\" class=\"data row8 col7\" >0.854445</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row8_col8\" class=\"data row8 col8\" >1</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row8_col9\" class=\"data row8 col9\" >0.30197</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8level0_row9\" class=\"row_heading level0 row9\" >000917</th>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row9_col0\" class=\"data row9 col0\" >0.605519</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row9_col1\" class=\"data row9 col1\" >0.607093</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row9_col2\" class=\"data row9 col2\" >0.659413</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row9_col3\" class=\"data row9 col3\" >0.573692</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row9_col4\" class=\"data row9 col4\" >0.496394</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row9_col5\" class=\"data row9 col5\" >0.420203</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row9_col6\" class=\"data row9 col6\" >0.618028</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row9_col7\" class=\"data row9 col7\" >0.424576</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row9_col8\" class=\"data row9 col8\" >0.30197</td>\n",
       "                        <td id=\"T_0d1a7058_1b81_11eb_8855_9cb6d0eebed8row9_col9\" class=\"data row9 col9\" >1</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1714098e048>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.corr().style.background_gradient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_ref = pd.read_csv('./refData/AllIndex.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>name</th>\n",
       "      <th>market</th>\n",
       "      <th>publisher</th>\n",
       "      <th>category</th>\n",
       "      <th>base_date</th>\n",
       "      <th>base_point</th>\n",
       "      <th>list_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>000829.CSI</td>\n",
       "      <td>沪深300低贝塔</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>策略指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20120806.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>000843.CSI</td>\n",
       "      <td>沪深300动态</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>策略指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20130107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>000844.CSI</td>\n",
       "      <td>沪深300稳定</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>策略指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20130107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>930767.CSI</td>\n",
       "      <td>沪深300优选</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>策略指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20151210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>930768.CSI</td>\n",
       "      <td>沪深300优选(HKD)</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>策略指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20151210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1326</th>\n",
       "      <td>h00828.CSI</td>\n",
       "      <td>沪深300高贝塔全收益</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>策略指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20120806.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>h00829.CSI</td>\n",
       "      <td>沪深300低贝塔全收益</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>策略指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20120806.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>h00843.CSI</td>\n",
       "      <td>沪深300动态全收益</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>策略指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20130107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>h00844.CSI</td>\n",
       "      <td>沪深300稳定全收益</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>策略指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20130107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2079</th>\n",
       "      <td>h20767.CSI</td>\n",
       "      <td>沪深300优选全收益</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>策略指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20151210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2080</th>\n",
       "      <td>h20768.CSI</td>\n",
       "      <td>沪深300优选(HKD)全收益</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>策略指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20151210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>000828.SH</td>\n",
       "      <td>沪深300高贝塔</td>\n",
       "      <td>SSE</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>策略指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20120806.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>000829.SH</td>\n",
       "      <td>沪深300低贝塔(SH)</td>\n",
       "      <td>SSE</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>策略指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20120806.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>000843.SH</td>\n",
       "      <td>沪深300动态(SH)</td>\n",
       "      <td>SSE</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>策略指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20130107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>000844.SH</td>\n",
       "      <td>沪深300稳定(SH)</td>\n",
       "      <td>SSE</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>策略指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20130107.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ts_code             name market publisher category   base_date  \\\n",
       "46    000829.CSI         沪深300低贝塔    CSI      中证公司     策略指数  20041231.0   \n",
       "58    000843.CSI          沪深300动态    CSI      中证公司     策略指数  20041231.0   \n",
       "59    000844.CSI          沪深300稳定    CSI      中证公司     策略指数  20041231.0   \n",
       "473   930767.CSI          沪深300优选    CSI      中证公司     策略指数  20041231.0   \n",
       "474   930768.CSI     沪深300优选(HKD)    CSI      中证公司     策略指数  20041231.0   \n",
       "1326  h00828.CSI      沪深300高贝塔全收益    CSI      中证公司     策略指数  20041231.0   \n",
       "1327  h00829.CSI      沪深300低贝塔全收益    CSI      中证公司     策略指数  20041231.0   \n",
       "1336  h00843.CSI       沪深300动态全收益    CSI      中证公司     策略指数  20041231.0   \n",
       "1337  h00844.CSI       沪深300稳定全收益    CSI      中证公司     策略指数  20041231.0   \n",
       "2079  h20767.CSI       沪深300优选全收益    CSI      中证公司     策略指数  20041231.0   \n",
       "2080  h20768.CSI  沪深300优选(HKD)全收益    CSI      中证公司     策略指数  20041231.0   \n",
       "174    000828.SH         沪深300高贝塔    SSE      中证公司     策略指数  20041231.0   \n",
       "175    000829.SH     沪深300低贝塔(SH)    SSE      中证公司     策略指数  20041231.0   \n",
       "183    000843.SH      沪深300动态(SH)    SSE      中证公司     策略指数  20041231.0   \n",
       "184    000844.SH      沪深300稳定(SH)    SSE      中证公司     策略指数  20041231.0   \n",
       "\n",
       "      base_point   list_date  \n",
       "46        1000.0  20120806.0  \n",
       "58        1000.0  20130107.0  \n",
       "59        1000.0  20130107.0  \n",
       "473       1000.0  20151210.0  \n",
       "474       1000.0  20151210.0  \n",
       "1326      1000.0  20120806.0  \n",
       "1327      1000.0  20120806.0  \n",
       "1336      1000.0  20130107.0  \n",
       "1337      1000.0  20130107.0  \n",
       "2079      1000.0  20151210.0  \n",
       "2080      1000.0  20151210.0  \n",
       "174       1000.0  20120806.0  \n",
       "175       1000.0  20120806.0  \n",
       "183       1000.0  20130107.0  \n",
       "184       1000.0  20130107.0  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_ref[(index_ref.name.str.startswith('沪深300'))&(index_ref.category==\"策略指数\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>name</th>\n",
       "      <th>market</th>\n",
       "      <th>publisher</th>\n",
       "      <th>category</th>\n",
       "      <th>base_date</th>\n",
       "      <th>base_point</th>\n",
       "      <th>list_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>000988.CSI</td>\n",
       "      <td>全指工业</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>000994.CSI</td>\n",
       "      <td>全指电信</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>000995.CSI</td>\n",
       "      <td>全指公用</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1425</th>\n",
       "      <td>h00986.CSI</td>\n",
       "      <td>全指能源全收益</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1426</th>\n",
       "      <td>h00987.CSI</td>\n",
       "      <td>全指材料全收益</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1427</th>\n",
       "      <td>h00988.CSI</td>\n",
       "      <td>全指工业全收益</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1428</th>\n",
       "      <td>h00989.CSI</td>\n",
       "      <td>全指可选全收益</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1429</th>\n",
       "      <td>h00990.CSI</td>\n",
       "      <td>全指消费全收益</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1430</th>\n",
       "      <td>h00991.CSI</td>\n",
       "      <td>全指医药全收益</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1431</th>\n",
       "      <td>h00992.CSI</td>\n",
       "      <td>全指金融全收益</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432</th>\n",
       "      <td>h00993.CSI</td>\n",
       "      <td>全指信息全收益</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>h00994.CSI</td>\n",
       "      <td>全指电信全收益</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1434</th>\n",
       "      <td>h00995.CSI</td>\n",
       "      <td>全指公用全收益</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2662</th>\n",
       "      <td>h30177.CSI</td>\n",
       "      <td>全指食品</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>二级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20130715.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>000057.SH</td>\n",
       "      <td>全指成长</td>\n",
       "      <td>SSE</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>成长指数</td>\n",
       "      <td>20031231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20100104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>000058.SH</td>\n",
       "      <td>全指价值</td>\n",
       "      <td>SSE</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>价值指数</td>\n",
       "      <td>20031231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20100104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>000986.SH</td>\n",
       "      <td>全指能源</td>\n",
       "      <td>SSE</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>000987.SH</td>\n",
       "      <td>全指材料</td>\n",
       "      <td>SSE</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>000988.SH</td>\n",
       "      <td>全指工业(SH)</td>\n",
       "      <td>SSE</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>000989.SH</td>\n",
       "      <td>全指可选</td>\n",
       "      <td>SSE</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>000990.SH</td>\n",
       "      <td>全指消费</td>\n",
       "      <td>SSE</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>000991.SH</td>\n",
       "      <td>全指医药</td>\n",
       "      <td>SSE</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>000992.SH</td>\n",
       "      <td>全指金融</td>\n",
       "      <td>SSE</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>000993.SH</td>\n",
       "      <td>全指信息</td>\n",
       "      <td>SSE</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>000994.SH</td>\n",
       "      <td>全指电信(SH)</td>\n",
       "      <td>SSE</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>000995.SH</td>\n",
       "      <td>全指公用(SH)</td>\n",
       "      <td>SSE</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>一级行业指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20110802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>h00057.SH</td>\n",
       "      <td>全指成长全收益</td>\n",
       "      <td>SSE</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>成长指数</td>\n",
       "      <td>20031231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20100104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>h00058.SH</td>\n",
       "      <td>全指价值全收益</td>\n",
       "      <td>SSE</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>价值指数</td>\n",
       "      <td>20031231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20100104.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ts_code      name market publisher category   base_date  base_point  \\\n",
       "129   000988.CSI      全指工业    CSI      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "130   000994.CSI      全指电信    CSI      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "131   000995.CSI      全指公用    CSI      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "1425  h00986.CSI   全指能源全收益    CSI      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "1426  h00987.CSI   全指材料全收益    CSI      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "1427  h00988.CSI   全指工业全收益    CSI      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "1428  h00989.CSI   全指可选全收益    CSI      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "1429  h00990.CSI   全指消费全收益    CSI      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "1430  h00991.CSI   全指医药全收益    CSI      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "1431  h00992.CSI   全指金融全收益    CSI      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "1432  h00993.CSI   全指信息全收益    CSI      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "1433  h00994.CSI   全指电信全收益    CSI      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "1434  h00995.CSI   全指公用全收益    CSI      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "2662  h30177.CSI      全指食品    CSI      中证公司   二级行业指数  20041231.0      1000.0   \n",
       "54     000057.SH      全指成长    SSE      中证公司     成长指数  20031231.0      1000.0   \n",
       "55     000058.SH      全指价值    SSE      中证公司     价值指数  20031231.0      1000.0   \n",
       "258    000986.SH      全指能源    SSE      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "259    000987.SH      全指材料    SSE      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "260    000988.SH  全指工业(SH)    SSE      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "261    000989.SH      全指可选    SSE      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "262    000990.SH      全指消费    SSE      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "263    000991.SH      全指医药    SSE      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "264    000992.SH      全指金融    SSE      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "265    000993.SH      全指信息    SSE      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "266    000994.SH  全指电信(SH)    SSE      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "267    000995.SH  全指公用(SH)    SSE      中证公司   一级行业指数  20041231.0      1000.0   \n",
       "341    h00057.SH   全指成长全收益    SSE      中证公司     成长指数  20031231.0      1000.0   \n",
       "342    h00058.SH   全指价值全收益    SSE      中证公司     价值指数  20031231.0      1000.0   \n",
       "\n",
       "       list_date  \n",
       "129   20110802.0  \n",
       "130   20110802.0  \n",
       "131   20110802.0  \n",
       "1425  20110802.0  \n",
       "1426  20110802.0  \n",
       "1427  20110802.0  \n",
       "1428  20110802.0  \n",
       "1429  20110802.0  \n",
       "1430  20110802.0  \n",
       "1431  20110802.0  \n",
       "1432  20110802.0  \n",
       "1433  20110802.0  \n",
       "1434  20110802.0  \n",
       "2662  20130715.0  \n",
       "54    20100104.0  \n",
       "55    20100104.0  \n",
       "258   20110802.0  \n",
       "259   20110802.0  \n",
       "260   20110802.0  \n",
       "261   20110802.0  \n",
       "262   20110802.0  \n",
       "263   20110802.0  \n",
       "264   20110802.0  \n",
       "265   20110802.0  \n",
       "266   20110802.0  \n",
       "267   20110802.0  \n",
       "341   20100104.0  \n",
       "342   20100104.0  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_ref[(index_ref.name.str.startswith('全指'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>name</th>\n",
       "      <th>market</th>\n",
       "      <th>publisher</th>\n",
       "      <th>category</th>\n",
       "      <th>base_date</th>\n",
       "      <th>base_point</th>\n",
       "      <th>list_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>000803.CSI</td>\n",
       "      <td>300波动</td>\n",
       "      <td>CSI</td>\n",
       "      <td>中证公司</td>\n",
       "      <td>策略指数</td>\n",
       "      <td>20041231.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>20120109.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ts_code   name market publisher category   base_date  base_point  \\\n",
       "28  000803.CSI  300波动    CSI      中证公司     策略指数  20041231.0      1000.0   \n",
       "\n",
       "     list_date  \n",
       "28  20120109.0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_ref.query('ts_code == \"000803.CSI\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "strategies = ['000828', '000918', '000919','000944']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n"
     ]
    }
   ],
   "source": [
    "df_style = pd.concat( [ts.get_k_data(sym, index=True) for sym in strategies])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_style['symbol'] = df_style['code'].str[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "close = df_style.reset_index().pivot_table('close', 'date', 'symbol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "returns_style = close.pct_change().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "rolling_corr = returns_style.rolling(250).corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17142b4d308>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rolling_corr.unstack(level=1)['000828'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample=returns.rolling(250).corr().unstack(level=1)['000910']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = sample.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17142979b48>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample['000916'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1714271a108>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(returns['000910'], returns['000916'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17144cf2c88>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(returns[['000910','000916']]+1).cumprod().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`fi_symbols` are the full_stock indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "fi_symbols = ['%06d'%i for i in range(986, 996) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tushare.stock import cons\n",
    "\n",
    "for sym in fi_symbols:\n",
    "    if sym not in cons.INDEX_SYMBOL:\n",
    "        cons.INDEX_SYMBOL[sym] = 'sh'+sym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n",
      "本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2\n"
     ]
    }
   ],
   "source": [
    "df_full = pd.concat( [ ts.get_k_data(sym, index=True) for sym in fi_symbols ] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full['symbol'] = df_full.code.str[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "close_full = df_full.pivot_table('close', 'date', 'symbol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "returns_full = close_full.pct_change().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_full = returns_full.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17144ee89c8>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "close_full['000988'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.bdate_range('2018-10-31', '2020-10-31', )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "cal = pro.trade_cal(exchange='', start_date='20181031', end_date='20201031')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "trading_dates = pd.to_datetime( cal.query('is_open==1').cal_date )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1714cd34948>"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(returns_full.reindex(dates)+1).cumprod().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly\n",
    "plotly.offline.init_notebook_mode(connected=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JupyterLab v1.2.3\n",
      "Known labextensions:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "   app dir: C:\\Users\\Josh\\AppData\\Local\\conda\\conda\\envs\\cvxpyenv\\share\\jupyter\\lab\n",
      "        jupyterlab-plotly v4.12.0 enabled  ok\n"
     ]
    }
   ],
   "source": [
    "!jupyter labextension list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.12.0'"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotly.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.line(close_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "symbol=000986<br>date=%{x}<br>value=%{y}<extra></extra>",
         "legendgroup": "000986",
         "line": {
          "color": "#636efa",
          "dash": "solid"
         },
         "mode": "lines",
         "name": "000986",
         "showlegend": true,
         "type": "scattergl",
         "visible": "legendonly",
         "x": [
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